Abstract

Multisensor-multitarget sensor management is at root a problem in nonlinear control theory. Single-sensor, single-target control typically employs a Kalman filter to predict future target states, in conjunction with a core objective function (usually, a Mahalanobis distance) that dynamically positions the sensor Field of View (FoV) over predicted target position. An earlier (1996) paper proposed a foundation for sensor management based on the Bayes recursive filter for the entire multisensor-multitarget system, used in conjunction with a multi-target Kullback-Leibler objective function. This chapter proposes a potentially computationally tractable approximation of this approach. We analyze possible single-step and multistep objective functions: general multitarget Csiszár information-theoretic functionals and “geometric” functionals, used with various optimization strategies (maxi-min, maxi-mean, and “maxi-null”). We show that some of these objective functions lead to potentially tractable sensor management algorithms when used in conjunction with МНС (multi-hypothesis correlator) algorithms.

Keywords

Covariance Radar Coherence Expense Lution

The work reported in this chapter was supported by the U.S. Army Research Office under contracts DAAН04–94-C-0011, DAAG55–98-C-0039 and the U.S. Air Force Office of Research under contract F49620–01-C-0031. The content does not necessarily reflect the position or the policy of the Government. No official endorsement should be inferred.